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From authentic products to authenticated processes: a systematic conceptual review of authentic assessment in AI-rich higher education
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DOI：10.1080/02602938.2026.2695376.png)
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Abstract
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This article presents a systematic conceptual review of authentic assessment in higher education and develops a six-dimension framework for assessment design in digitally mediated and AI-rich conditions. Drawing on a retained corpus of 37 substantive sources, it synthesises foundational and contemporary literature on task fidelity, evaluative judgement, process evidence, inclusion, and AI-mediated validity. The synthesis shows that authentic assessment should not be reduced to workplace simulation or treated primarily as a response to academic misconduct. It is better understood as a multidimensional design orientation spanning contextual fidelity and consequential relevance, cognitive demand and evaluative judgement, process transparency and integrity, student agency and bounded choice, inclusivity and representational fairness, and AI-aware validity and ethical practice. The article’s main contribution is to distinguish authentic products from authenticated processes. It argues that assessment validity under generative AI depends not only on realistic outputs, but on architectures that make human judgement, verification, and responsibility visible. The framework offers review questions that support module-level and programme-level redesign by linking authenticity, evidence, validity, and accountable student judgement.
Keywords:
Authentic assessment
higher education
assessment design
generative AI
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